High-fidelity and rapid cellular-level Mueller matrix imaging for tissue identification with unstained sections

Mueller matrix polarimetry is regarded as a promising technique in the field of biomedicine, especially for pathological diagnosis. However, the current studies on Mueller imaging of pathological sections are all at the tissue-level, and the cellular-level polarization information is difficult to ob...

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Veröffentlicht in:Biomedical optics express 2021-08, Vol.12 (8), p.4745-4758
Hauptverfasser: Wang, Jiazhi, Li, Yanqiu, Cao, Chenle, Zhou, Guodong, Li, Li
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Li, Yanqiu
Cao, Chenle
Zhou, Guodong
Li, Li
description Mueller matrix polarimetry is regarded as a promising technique in the field of biomedicine, especially for pathological diagnosis. However, the current studies on Mueller imaging of pathological sections are all at the tissue-level, and the cellular-level polarization information is difficult to obtain. To overcome this challenge, we first propose a cellular-level Mueller matrix imaging method for accurate quantitative identification of tissues in this study. Benefiting from the significant birefringent behavior of paraffin in unstained sections, the proposed method can locate the paraffin distribution areas of retardance images by involving Otsu's algorithm. Then, the real cellular-level polarization information (e.g., depolarization) is acquired. The efficiency of the proposed method was demonstrated on unstained rat tissue samples. The results showed that the obtained depolarization images are highly consistent with the stained microscopic images in terms of the morphology and arrangement of the tissues at cellular level. Finally, this method was preliminarily applied to the detection of human lung cancer tissue section, effectively realizing the quantitative differentiation of normal, inflamed, and malignant areas in unstained section. This study provides a possible approach for the rapid and accurate diagnosis of cancer.
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title High-fidelity and rapid cellular-level Mueller matrix imaging for tissue identification with unstained sections
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